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Abstract:
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Epidemiological models traditionally make the assumption that populations are homogeneous . By relaxing that assumption , models often become more complicated , but better representations of the real world . Here we describe new computational tools for studying heterogeneous populations , and we examine consequences of two particular types of heterogeneity : that people are not all equally likely to interact , and that people are not all equally likely to become infected if exposed to a pathogen .
Contact network epidemiology provides a robust and flexible paradigm for thinking about heterogeneous populations . Despite extensive mathematical and algorithmic methods , however , we lack a programming framework for working with epidemiological contact networks and for the simulation of disease transmission through such networks . We present EpiFire , a C++ applications programming interface and graphical user interface , which includes a fast and efficient library for generating , analyzing and manipulating networks . EpiFire also provides a variety of traditional and network -based epidemic simulations .
Heterogeneous population structure may cause multi -wave epidemics , but urban populations are generally assumed to be too well mixed to have such structure . Multi -wave epidemics are not predicted by simple models , and are particularly problematic for public health officials deploying limited resources . Using a unique empirical interaction network for 103 ,000 people in Montreal , Canada , we show that large , urban populations may feature sufficient community structure to drive multi -wave dynamics , and that highly connected individuals may play an important role in whether communities are synchronized .
Finally , we show that heterogeneous immunity is an important determinant of influenza epidemic size . While many epidemic models assume a homogeneously susceptible population and describe dynamics for one season , the trans -seasonal dynamics of partially immunizing diseases likely play a critical role in determining both future epidemic size and pathogen evolution . We present a multi -season network model of a population exposed to a pathogen conferring partial cross -immunity that decays over time . We fit the model to 25 years of influenza -like illness epidemic data from France using a novel Bayesian technique . Using conservative priors , we estimate important epidemiological quantities that are consistent with empirical studies . |